Article
Multidisciplinary Sciences
Ke Zhang, Wanwan Feng, Peng Wang
Summary: The authors develop a highly scalable method, scGCO, to identify genes whose expression values form spatial patterns from spatial transcriptomics data.
NATURE COMMUNICATIONS
(2022)
Article
Biochemical Research Methods
Yuanyuan Chen, Xiaodan Fan, Cong Pian
Summary: This article introduces a method for identifying functional (or disease-relevant) modules using gene expression data by integrating gene interaction networks and energy minimization with graph cuts method. The method is successful in identifying disease-relevant modules and performs well in real experiments.
CURRENT BIOINFORMATICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Marina Marceta, Tibor Lukic
Summary: This paper introduces graph cuts based computed tomography reconstruction methods with shape circularity regularization and energy-minimization algorithm, suitable for binary tomography with limited projection views. Experimental evaluation results for both binary and multi-level tomography are presented.
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2022)
Article
Physics, Multidisciplinary
Quintino Francesco Lotito, Federico Musciotto, Alberto Montresor, Federico Battiston
Summary: Recent research has shown that pairwise interactions in a given network are replaced by higher-order interactions. This study develops an algorithm to detect patterns in hypergraphs and demonstrates how they can be used to identify structural differences in various real-world systems.
COMMUNICATIONS PHYSICS
(2022)
Article
Computer Science, Theory & Methods
Amir Abboud, Keren Censor-Hillel, Seri Khoury, Ami Paz
Summary: This article introduces the concept of "bit-gadget" to prove strong lower bounds for distributed computing in the congest model through constructing graphs with small cuts. The contribution of bit-gadgets is twofold, extending known techniques to show near-linear lower bounds for computing the diameter and improving approximations for various distance computation problems. Additionally, small cuts are essential for proving super-linear lower bounds, with the potential for near-quadratic lower bounds for certain problems.
ACM TRANSACTIONS ON ALGORITHMS
(2021)
Article
Physics, Multidisciplinary
Zhuo Chen, Hongyu Yang, Yanli Liu
Summary: The paper proposes an order reduction design framework for optimizing higher-order binary Markov random fields (HoMRFs), which is widely used in various domains. The framework decomposes the design difficulty into two processes and provides a new family of 14 order reduction methods. Experiments demonstrate the superiority of the proposed method.
Article
Computer Science, Artificial Intelligence
Jianxin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He
Summary: Graph neural networks (GNNs) are widely used in deep learning for graph analysis tasks. However, current methods ignore heterogeneity in real-world graphs and fail to capture content-based correlations between nodes. In this paper, we propose a novel HAE framework and a HAE(GNN) model that incorporates meta-paths and meta-graphs for rich, heterogeneous semantics and leverages self-attention mechanism for exploring content-based interactions between nodes.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Panagiotis C. Petrantonakis
Summary: This study extends the concept of Higher Order Crossings (HOC) analysis to graph signals and defines the HOCg sequence. By combining HOCg with a Support Vector Machine classifier, the proposed method achieves fast and similar or better performance in graph signal discrimination compared to state-of-the-art algorithms across five benchmark datasets.
IEEE SIGNAL PROCESSING LETTERS
(2021)
Article
Engineering, Multidisciplinary
Ru Huang, Zijian Chen, Guangtao Zhai, Jianhua He, Xiaoli Chu
Summary: This paper proposes a graph representation framework called Greet based on graph entropy. Greet achieves accurate calculation of graph information entropy and demonstrates superior performance in graph classification and clustering tasks.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Wenfei Fan, Yuanhao Li, Muyang Liu, Can Lu
Summary: The paper proposes a scheme to reduce big graphs to compact form by merging supernodes and creating synopses, improving query efficiency while maintaining data integrity. Experimental results show that the contraction scheme significantly reduces graph size and enhances evaluation for various types of queries.
Article
Engineering, Electrical & Electronic
Zeeshan Akhtar, Ketan Rajawat
Summary: This paper discusses stochastic convex optimization problems with two sets of constraints and proposes corresponding solution methods. By utilizing momentum-based gradient tracking technique, the proposed algorithms achieve faster convergence rates and their effectiveness is demonstrated through experiments.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Review
Computer Science, Information Systems
Meirav Zehavi
Summary: This article provides an introductory overview of Parameterized Complexity, offering readers without background knowledge a simplified understanding. It focuses on analyzing crossing minimization problems in Graph Drawing from a parameterized analysis viewpoint and highlights the remaining challenges. The article originated from an invited talk presented at the 29th International Symposium on Graph Drawing and Network Visualization.
COMPUTER SCIENCE REVIEW
(2022)
Article
Multidisciplinary Sciences
Beibei Zhu, Hongji Zhou
Summary: This paper proposes higher-order adaptive energy-preserving methods to address the energy drift problem in methods based on the average vector field. Two adaptive algorithms are developed using variable points and different step sizes. The numerical results demonstrate that these adaptive algorithms perform better in terms of phase portrait and energy conservation compared to the Runge-Kutta methods.
Article
Physics, Multidisciplinary
Ann S. Blevins, Jason Z. Kim, Dani S. Bassett
Summary: This study investigates the interplay between noise and data in networks, showing that the structure of low-weight, noisy edges varies according to the topology of the model network to which it is added. The authors propose a method to use these noisy edges to classify model networks, demonstrating that noise can be a useful entity in characterizing higher-order network interactions.
COMMUNICATIONS PHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Jianliang Gao, Jun Gao, Xiaoting Ying, Mingming Lu, Jianxin Wang
Summary: In this paper, we propose a Substructure Assembling Graph Attention Network (SA-GAT) to extract graph features and improve the performance of graph classification. SA-GAT is able to fully explore higher-order substructure information hidden in graphs by a core module called Substructure Interaction Attention (SIA). Theoretical analysis also shows that SA-GAT satisfies the graph isomorphism theory of graph neural network design.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
